Conventionally, a method using a sample variance usually defined in statistics is used in order to detect an original image feature and a noise feature in an input image. In this conventional method, image sample data that has a small sample variance is detected as a noise feature and image sample data that has a large sample variance is detected as an image feature.
Referring to FIG. 1, sample (a) is an enlarged view of a part of an image having a small detail of an original image and having a slight of Gaussian noise while sample (b). is an enlarged view of a part of an image having only Gaussian noise without an image feature. Both of the samples (a) and (b) have similar sample variances of about 25.
Referring to FIG. 2, sample (a) corresponds to an edge while sample (b) shows an image having only Gaussian noise without an image feature. However, both of the samples (a) and (b) have similar sample variances of about 2500.
It can be inferred from samples (a) and (b) illustrated in FIGS. 1 and 2 that there is a limit in detecting an image feature or a noise feature in an input image based on only the value of a sample variance. Moreover, since most image processing application systems do not possess advance information on the variance of noise complying with the Gaussian distribution N(0, σ2), it is difficult to provide coherent results regardless of the value of the variance of noise.